FIntech AI Fraud Detection Solution

Every transaction your platform processes carries risk signals - location, device, amount, velocity, user history, merchant, and network behavior. Our real-time scoring engine evaluates these simultaneously, producing a fraud risk score in milliseconds. The system applies supervised ML models trained on your transaction data and enriched with external fraud intelligence networks. It runs inline, meaning it decides before settlement, not as a retrospective audit. This is the core of your fraud defense for payment platforms processing thousands of transactions per second.

Phishing, credential stuffing, and SIM swap attacks are the most common routes into a digital account. ATO not only costs you the fraud loss, but it also destroys customer trust. Our ATO prevention layer fuses device fingerprinting, behavioral biometrics (keystroke cadence, mouse movement, touch dynamics), and continuous session risk scoring. If a session diverges from an established behavior baseline, a different device, an unusual navigation sequence, or a new geography, risk scores increase and step-up authentication triggers automatically. This detects takeovers that password-reset flows and static IP checks entirely miss. For eWallet platforms and neobanks, ATO prevention is built directly into the app layer.

Synthetic identity fraud begins at onboarding. Fraudsters combine real SSNs, fabricated names, and DOBs to create personas that pass basic document checks but leave behavioral traces a trained model can catch. Our AI-powered KYC fraud detection layer cross-references identity elements across various data sources, applies document forensics to detect manipulated IDs, and uses predictive risk scoring during sign-up before an account is ever funded. This reduces onboarding fraud without adding friction that drives away genuine customers. It also feeds your AML program with cleaner entity data from day one.

Rules-based transaction monitoring was never designed to catch modern layering and structuring schemes. Our AI-powered AML monitoring applies graph analytics to map the complete network of relationships around each entity - shared devices, connected accounts, and overlapping transaction patterns. It detects smurfing, structuring, complex multi-hop laundering chains, and round-trip transactions that single-transaction analysis can't see. The system generates SAR-ready case files, maintains full audit trails, and produces regulator-readable reports. This aligns with FinCEN SAR filing requirements, RBI KYC/AML master directions, FCA Financial Crime Guide obligations, and EU AMLD6.

Friendly fraud, where a genuine cardholder disputes a legitimate transaction, is one of the most costly and underreported fraud categories in BNPL and digital commerce. Our system builds longitudinal behavioral profiles of each user, flagging accounts revealing patterns consistent with friendly fraud: purchase timing, dispute frequency, refund behavior, and category shifts. It also interprets third-party payment fraud before chargebacks occur by applying pre-authorization risk scoring at the checkout layer. For BNPL platforms, this directly safeguards your merchant relationships and diminishes your dispute rate.

First-party fraud in lending is where a borrower applies with an accurate identity but no intent to repay. It's impossible to catch with traditional credit scoring. Bust-out fraud, where a borrower creates a clean credit history then maxes out all credit lines simultaneously, follows identifiable patterns in application behavior and credit utilization. Our ML models score loan applications against application velocity, behavioral signals, device consistency, and bureau data anomalies. This complements your underwriting process rather than replacing it, giving your risk team a second layer of signal on every application. Lending software developed by Nimble AppGenie can have this embedded from day one.